Summary of Hiagent: Hierarchical Working Memory Management For Solving Long-horizon Agent Tasks with Large Language Model, by Mengkang Hu et al.
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model
by Mengkang Hu, Tianxing Chen, Qiguang Chen, Yao Mu, Wenqi Shao, Ping Luo
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces HiAgent, a framework that enhances the working memory of Large Language Model (LLM)-based agents in various domains. The effectiveness of these agents is influenced by their memory mechanism, which records historical experiences as sequences of action-observation pairs. Existing approaches often involve directly inputting entire historical action-observation pairs into LLMs, leading to redundancy in long-horizon tasks. HiAgent leverages subgoals as memory chunks to manage working memory hierarchically, prompting LLMs to formulate subgoals before generating executable actions and enabling LLMs to decide proactively to replace previous subgoals with summarized observations. Experimental results across five long-horizon tasks demonstrate that HiAgent achieves a twofold increase in success rate and reduces the average number of steps required by 3.8, highlighting its robustness and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving computers that can learn from experience. These computers are like super smart robots that can make decisions based on what they’ve learned before. The problem is that these robots often get stuck in a loop, repeating the same actions over and over again. This paper introduces a new way of making these robots work better by breaking down big tasks into smaller ones, called subgoals. By doing this, the robots can learn faster and make better decisions. In experiments, this new approach worked really well, getting twice as many things right and using fewer steps to get there. This could help us build smarter computers that can do more complex tasks in the future. |
Keywords
» Artificial intelligence » Large language model » Prompting
